"""Behavioral fingerprint collector — consistency, instruction compliance, HTTP headers."""

import re
from typing import Dict, List
from core.client import AIClient
from core.models import BehavioralFingerprint, CollectionConfig
from utils.text_analysis import text_similarity


# Prompts for consistency testing (repeated multiple times)
CONSISTENCY_PROMPTS = [
    "In exactly 3 sentences, explain what an API is.",
    "List exactly 5 programming languages and nothing else.",
    "Translate 'Hello, how are you?' to French, Spanish, and German. Give only the translations.",
]

# Instruction compliance tests with verification functions
INSTRUCTION_TESTS = [
    {
        "prompt": "Respond with exactly the word 'HELLO' and nothing else.",
        "check": lambda text: text.strip().upper() == "HELLO",
        "name": "exact_word",
    },
    {
        "prompt": "Write a sentence that contains exactly 10 words.",
        "check": lambda text: abs(len(re.findall(r'\b\w+\b', text.strip().split('\n')[0])) - 10) <= 1,
        "name": "word_count",
    },
    {
        "prompt": "List 3 colors, one per line, with no numbering or bullets.",
        "check": lambda text: (
            2 <= len([l for l in text.strip().split('\n') if l.strip()]) <= 4
            and not any(re.match(r'^\s*[\d\-\*\u2022]', l) for l in text.strip().split('\n') if l.strip())
        ),
        "name": "format_compliance",
    },
    {
        "prompt": 'Answer in JSON format: {"name": "your_name", "type": "AI"}',
        "check": lambda text: '{' in text and '}' in text and '"name"' in text,
        "name": "json_format",
    },
    {
        "prompt": "Start your response with the word 'Actually' and explain why the sky is blue in 2 sentences.",
        "check": lambda text: text.strip().lower().startswith("actually"),
        "name": "start_word",
    },
]

# Headers of interest for fingerprinting
INTERESTING_HEADERS = [
    "server",
    "x-request-id",
    "x-ratelimit-limit-requests",
    "x-ratelimit-limit-tokens",
    "cf-ray",
    "cf-cache-status",
    "x-cloud-trace-context",
    "via",
    "x-powered-by",
    "x-served-by",
    "request-id",
    "anthropic-ratelimit-requests-limit",
    "anthropic-ratelimit-tokens-limit",
]


async def collect_behavioral(client: AIClient, config: CollectionConfig,
                              progress_callback=None) -> BehavioralFingerprint:
    """
    Collect behavioral fingerprint from an AI API channel.

    Tests response consistency, instruction compliance, and HTTP header patterns.
    """
    consistency_scores: List[float] = []
    instruction_compliance: Dict[str, bool] = {}
    response_headers: Dict[str, str] = {}

    total_tasks = (len(CONSISTENCY_PROMPTS) * config.repeat_count
                   + len(INSTRUCTION_TESTS) + 1)  # +1 for header collection
    completed = 0

    # === Consistency testing ===
    for prompt_idx, prompt in enumerate(CONSISTENCY_PROMPTS):
        responses: List[str] = []

        for repeat in range(config.repeat_count):
            try:
                text, _, headers = await client.send_message(
                    prompt=prompt,
                    max_tokens=256,
                    temperature=0.0,  # Deterministic for consistency testing
                )
                responses.append(text)

                # Capture headers from first successful response
                if not response_headers and headers:
                    for key in INTERESTING_HEADERS:
                        for h_key, h_val in headers.items():
                            if h_key.lower() == key.lower():
                                response_headers[key] = h_val

            except Exception as e:
                if progress_callback:
                    progress_callback(f"  ⚠ Consistency prompt {prompt_idx+1} repeat {repeat+1} failed: {e}")

            completed += 1
            if progress_callback:
                progress_callback(f"  Behavioral: {completed}/{total_tasks}")

        # Calculate pairwise similarity between responses
        if len(responses) >= 2:
            pair_scores = []
            for i in range(len(responses)):
                for j in range(i + 1, len(responses)):
                    sim = text_similarity(responses[i], responses[j])
                    pair_scores.append(sim)

            avg_consistency = sum(pair_scores) / len(pair_scores)
            consistency_scores.append(avg_consistency)

    # === Instruction compliance testing ===
    for test in INSTRUCTION_TESTS:
        try:
            text, _, headers = await client.send_message(
                prompt=test["prompt"],
                max_tokens=256,
            )

            try:
                passed = test["check"](text)
            except Exception:
                passed = False

            instruction_compliance[test["name"]] = passed

            # Update headers if needed
            if not response_headers and headers:
                for key in INTERESTING_HEADERS:
                    for h_key, h_val in headers.items():
                        if h_key.lower() == key.lower():
                            response_headers[key] = h_val

        except Exception as e:
            instruction_compliance[test["name"]] = False
            if progress_callback:
                progress_callback(f"  ⚠ Instruction test '{test['name']}' failed: {e}")

        completed += 1
        if progress_callback:
            progress_callback(f"  Behavioral: {completed}/{total_tasks}")

    # === Additional header collection via a simple request ===
    if not response_headers:
        try:
            _, _, headers = await client.send_message(
                prompt="Say 'hello'.",
                max_tokens=16,
            )
            if headers:
                for key in INTERESTING_HEADERS:
                    for h_key, h_val in headers.items():
                        if h_key.lower() == key.lower():
                            response_headers[key] = h_val
        except Exception:
            pass

    completed += 1
    if progress_callback:
        progress_callback(f"  Behavioral: {completed}/{total_tasks}")

    return BehavioralFingerprint(
        consistency_scores=consistency_scores,
        instruction_compliance=instruction_compliance,
        response_headers=response_headers,
    )